Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study

Abstract

Objective: To compare and evaluate radiomics models to preoperatively predict b-catenin mutation in patients with hepatocellular carcinoma (HCC).Methods: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models.Results: Among all 15 types of radiomics models, the model with the best performance was seen in the R HBP radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the R HBP radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75-0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63-0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the R HBP radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best R HBP radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low Frontiers in Oncology frontiersin.org 01

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

“…Notably, these works studied expression of vascular endothelial growth factor (VEGF) ( 56 ), angiopoietin-2 ( 57 ), Forkhead Box M1 (FOXM1) ( 58 ), and Ki-67 ( 59 , 60 ). Additionally, the presence of β-catenin mutation ( 61 ), intra-tumoral tertiary lymphoid structures ( 62 ), cytokeratin 19 ( 63 , 64 ), glypican-3 (GPC3) ( 65 ), immunohistochemical cell type markers for T-cells (CD3), macrophages (CD68) and endothelial cells (CD31), PD1 and CTLA4 at mRNA expression level ( 66 ), as well as density of CD3+ and CD8+ T cells ( 67 ) were studied. All the aforementioned molecules have relevant task in carcinogenesis.…”

“…Each of these immune subtypes plays a critical role in unraveling the complex immune response within HCC, providing insights for prognostication and targeted therapeutic interventions. AUCs of these tasks fell somewhere between 0.76 to 0.95 (56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67). Notably, when clinical factors were integrated with radiomics signatures, models' performance significantly improved.…”

MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

“…47 Other biomarkers associated with the HCC subtype or prognosis have also been explored using a radiomics approach, including VEGF expression and β-catenin mutation. 48,49 Furthermore, the tumor immune microenvironment may guide immunotherapy and reflect the prognosis in HCC patients. Some previous studies have started using MRI radiomics approaches to predict tumor immune infiltration or explore correlations with tumor immune cell expression.…”

“…The radiomics model derived from arterial and portal venous phases could predict non‐dual‐phenotype HCC (both CK7 and CK19 negative) with an AUC of 0.91 in the validation set 47 . Other biomarkers associated with the HCC subtype or prognosis have also been explored using a radiomics approach, including VEGF expression and β‐catenin mutation 48,49 . Furthermore, the tumor immune microenvironment may guide immunotherapy and reflect the prognosis in HCC patients.…”